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Thesis Tide

Thesis Tide ranks papers based on their relevance to the fields, with the goal of making it easier to find the most relevant papers. It uses AI to analyze the content of papers and rank them!

Properties of the one-component Coulomb gas on a sphere with two macroscopic external charges

The one-component Coulomb gas on the sphere, consisting on $N$ unit charges interacting via a logarithmic potential, and in the presence of two external charges each of strength proportional t...

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This article presents significant theoretical advancements in the understanding of one-component Coulomb gases, particularly regarding complex interactions in unique geometrical configurations. The use of advanced mathematical tools, like conformal maps and duality relations from random matrix theory, suggests a rigorous methodological approach that can form a foundation for future research in statistical mechanics and electrostatics. The distinction between the pre-critical and post-critical phases addresses crucial properties affecting phase transitions and energy interactions, which can stimulate further inquiries in both applied and theoretical physics.

Electron-phonon coupling in lattice engineering of lithium niobate single crystal thin films

Lithium niobate (LN) single crystal thin films are a high-performance photonic platform with applications in electro-optic modulators, nonlinear optical devices, optical frequency combs, and acousto-o...

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This article presents innovative methodologies focused on the significant properties of lithium niobate thin films, addressing critical challenges in the field of photonics. The emphasis on electron-phonon coupling introduces novel insights into the material's electronic structures, which could lead to groundbreaking applications in various technologies such as quantum computing and optical communications. The methodological rigor showcased through advanced analytical techniques further strengthens its potential impact on future research directions in this field.

Next-to-leading-order time-like rho electromagnetic form factors in ${k_{\rm T}}$ factorization

We calculate the time-like $ρ$ electromagnetic (EM) form factor in the $k_T$ factorization formalism by including the next-to-leading-order (NLO) corrections of the leading-twist and s...

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The article presents an advanced calculation of the rho meson electromagnetic form factors that incorporates next-to-leading-order corrections, which adds significant value to the existing theoretical frameworks. The acknowledgment of dominant twist-3 contributions over twist-2 is particularly novel and relevant in deepening the understanding of QCD effects in meson interactions. Its consistency with experimental results from the BABAR Collaboration enhances its credibility and applicability.

Simultaneous emulation and downscaling with physically-consistent deep learning-based regional ocean emulators

Building on top of the success in AI-based atmospheric emulation, we propose an AI-based ocean emulation and downscaling framework focusing on the high-resolution regional ocean over Gulf of Mexico. R...

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This article presents a novel approach to deep learning applications in ocean emulation, particularly emphasizing consistency with physical laws. The integration of autoregressive modeling and bias correction is innovative and addresses critical challenges in ocean modeling. Its applicability to high-resolution simulations has significant implications for climate science and regional ocean studies, potentially influencing future research directions in both AI and oceanography.

LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models

Recent advancements in reinforcement learning (RL) demonstrate the significant potential in autonomous driving. Despite this promise, challenges such as the manual design of reward functions and low s...

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The introduction of LearningFlow presents a significant advancement in the autonomy of urban driving systems. The novel integration of LLMs in the reinforcement learning process, particularly in automating the design of reward functions and training curricula, addresses longstanding challenges in the field. Its potential for high sample efficiency and adaptability across RL algorithms enhances its applicability, making it a highly impactful contribution to autonomous driving research.

Rudin Inequality, Chang Theorem, primes and squares

We prove that the set of large values of the trigonometric polynomial over a subset of density of the primes has some additive structure, similarly to what happens for subsets of densities in $\ma...

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The study presents a novel approach to understanding additive structures in sets with densities related to primes and squares, which is an uncommon area in analytic number theory. The application of large sieve inequalities to circle points adds significant methodological rigor and could lead to original insights. Its relevance extends not only to prime number theory but also to related areas in harmonic analysis and additive combinatorics, making it a meaningful contribution likely to inspire further research.

TAPFed: Threshold Secure Aggregation for Privacy-Preserving Federated Learning

Federated learning is a computing paradigm that enhances privacy by enabling multiple parties to collaboratively train a machine learning model without revealing personal data. However, current resear...

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TAPFed presents a significant advancement in the domain of federated learning by addressing critical vulnerabilities associated with malicious actors and inference attacks. The proposed threshold functional encryption scheme offers a novel approach to preserving privacy while maintaining model performance, making it beneficial for real-world applications. The rigorous security analyses, alongside comparative evaluations against state-of-the-art methods, underscore both the methodological rigor and practical relevance of the work.

Cover-Relax-Search: A Primal Heuristic for Binary Quadratic Programs

Binary Quadratic Programs (BQPs) are a class of NP-hard problems that arise in a wide range of applications, including finance, machine learning, and logistics. These problems are challenging to solve...

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The proposed Cover-Relax-Search algorithm addresses a significant challenge in the optimization of Binary Quadratic Programs, a class known for its NP-hardness and widespread application. Its novel approach, which improves upon existing heuristics by leveraging techniques from local search algorithms, offers a promising advancement in algorithmic efficiency and solution quality. The rigorous evaluation against various benchmarks further supports its potential impact within the field.

On the Generalizability of Transformer Models to Code Completions of Different Lengths

The programming landscape is nowadays being reshaped by the advent of Large Language Models (LLMs) able to automate code-related tasks related to code implementation (e.g., code completion) and compre...

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The article addresses a significant gap in the understanding of how transformer models perform in code completion tasks, particularly regarding the generalizability to code sequences of varying lengths. The empirical study conducted is rigorous and sheds light on an under-explored aspect of LLMs that could influence their use in software development. It raises critical questions about the training processes of LLMs and their implications for sustainability, making it both relevant and novel in its field.

Music Tagging with Classifier Group Chains

We propose music tagging with classifier chains that model the interplay of music tags. Most conventional methods estimate multiple tags independently by treating them as multiple independent binary c...

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This article proposes a novel method for music tagging that addresses the limitations of conventional independent binary classification approaches. The introduction of classifier group chains offers a significant advancement in the field, as it considers the conditional dependencies between tags. The systematic evaluation of the method through a well-established dataset further adds to the robustness and originality of the work, indicating its potential to improve tagging performance and influence future research in similar areas.

On the Boxicity of Line Graphs and of Their Complements

The boxicity of a graph is the smallest dimension $d$ allowing a representation of it as the intersection graph of a set of $d$ -dimensional axis-parallel boxes. We present a simple gen...

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The study presents a novel approach to determine the boxicity of graphs, specifically focusing on well-known graphs like the Petersen and Kneser graphs. Its method is both innovative and applicable, potentially leading to polynomial-time algorithms for otherwise NP-hard problems related to graph theory. This significance enhances its impact on the field, especially in combinatorial optimization and algorithm design.

Approximate Minimum Tree Cover in All Symmetric Monotone Norms Simultaneously

We study the problem of partitioning a set of $n$ objects in a metric space into $k$ clusters $V_1,\dots,V_k$ . The quality of the clustering is measured by considering the vect...

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The paper presents a novel combinatorial algorithm that effectively addresses the challenge of clustering in metric spaces using multiple symmetric norms, a significant advancement over previous work. Its applicability to large datasets (with 10,000 points in under a second) highlights its practical relevance. Additionally, the connection made to APX-hardness strengthens the theoretical foundation of the work, making it not only relevant for practitioners but also for further theoretical exploration.

Magnetization reversal of finite-length Co and Fe atomic chains on Pt(332) surface: numerical calculations and a new theoretical approach

Different mechanisms of magnetization reversal in finite-length Co and Fe chains on the Pt(332) surface have been investigated, taking into account the Dzyaloshinskii-Moriya interaction. It has been f...

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This article explores advanced concepts in magnetization reversal mechanisms using both numerical methods and a novel theoretical approach, providing significant insights into the behavior of magnetic materials. The integration of the Dzyaloshinskii-Moriya interaction and the application of the geodesic nudged elastic band method strengthen the methodological rigor. The broad applicability of the proposed theoretical framework to various one-dimensional magnetic systems indicates high interdisciplinary value, making it potentially transformative for future studies in magnetism.

Quantum-Assisted Space Logistics Mission Planning

Quantum computing provides a novel approach to addressing conventionally intractable issues in large-scale optimization. Space logistics missions require the efficient routing of payloads, spacecraft,...

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The paper presents a novel approach using quantum computing for space logistics, specifically tackling a critical and complex problem with significant practical implications. It leverages advanced methodologies not typically applied in this domain, increasing its novelty and potential impact. The methodological rigor is evident in the application of entropy quantum computing to a real-world issue, showcasing the applicability of quantum algorithms in aerospace contexts. The insights gained could inspire further research in both quantum computing and space mission planning, potentially leading to more efficient logistical operations in aerospace.

Frobenius--Perron dimension via $τ$ -tilting theory

From the viewpoint of $τ$ -tilting theory, we study Frobenius--Perron dimensions of finite-dimensional algebras. First, we evaluate the Frobenius--Perron dimensions of $τ$ -tilting finit...

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This article presents a significant advancement in understanding Frobenius--Perron dimensions through a systematic approach using $τ$-tilting theory. The novelty lies in applying combinatorial methods to evaluate these dimensions, which could lead to further developments in representation theory and algebra. The methodological rigor is supported by a detailed analysis of specific algebraic structures. Its findings may influence ongoing research in finite-dimensional algebras and representation theory, making it a highly relevant contribution.

Material instability and subsequent restabilization from homogenization of periodic elastic lattices

Two classes of non-linear elastic materials are derived via two-dimensional homogenization. These materials are equivalent to a periodic grid of axially-deformable and axially-preloaded structural ele...

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The article presents a novel approach to understanding material stability and design by exploring the concept of tuneable instabilities in elastic lattices. The methodological rigor in deriving two classes of materials and the implications for architected materials underscore its impact. Its potential to inspire future research in material science and engineering, particularly in the context of reconfigurable materials, adds to its relevance.

Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks

Thermally-driven semi-crystalline polymer networks are capable to achieve both the one-way shape-memory effect and two-way shape-memory effect under stress and stress-free conditions, therefore repres...

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The article presents a novel finite strain continuum model that provides a comprehensive understanding of shape-memory effects in complex polymer networks, which is particularly relevant for both academia and industry. The rigorous theoretical framework and validation against experimental data enhance its reliability and applicability, indicating a strong potential for influencing future research in polymer science and engineering. Furthermore, the model's adaptability to various polymer systems fosters interdisciplinary collaboration across materials science, mechanical engineering, and applied physics.

Observational Study of the Atmospheric Gravity Waves in the lower Solar Atmosphere

The solar chromosphere exhibits a variety of waves originating from the photosphere and deeper layers, causing oscillations at different heights with distinct frequencies. This study identifies and an...

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This study presents a novel investigation into the propagation and dynamics of Atmospheric Gravity Waves (AGWs) in the solar chromosphere, revealing important correlations with magnetic fields and spicules. The methodological rigor of using advanced spectroscopic observations and power map analyses enhances the credibility of the findings. The insights gained have implications not only for solar physics but also for the understanding of plasma dynamics in astrophysical contexts.

Quantum Birkhoff Normal Form in the $σ$ -Bruno-Rüssmann non-resonant condition

The aim of this paper is to construct a Gevrey quantum Birkhoff normal form for the $h$ -differential operator $P_{h}(t),$ where $t\in(-\frac{1}{2},\frac{1}{2})$ , in the neighb...

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This paper presents a significant advancement in the mathematical understanding of quantum mechanics through the lens of Birkhoff normal forms, potentially impacting the field of dynamical systems. The novel approach of applying the $σ$-Bruno-Rüssmann condition to the construction of a quantum normal form shows methodological rigor and introduces an interesting intersection of quantum theory and classical dynamics, suggesting promising pathways for future research.

SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution

Large Language Models (LLMs) have demonstrated remarkable proficiency across a variety of complex tasks. One significant application of LLMs is in tackling software engineering challenges, particularl...

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The article presents a novel open-source approach (SWE-Fixer) that addresses critical issues in software engineering, particularly with the accessibility and applicability of LLMs for GitHub issue resolution. Its methodological rigor is demonstrated through extensive benchmarking against state-of-the-art models and the compilation of a large dataset. The open-source nature enhances transparency and reproducibility, which are crucial for the research community.